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Ed Helper

Ed Helper#

This notebook is meant to assist with Between-Class Participation grading. To use:

  1. Download the Discussion data

    1. Go to Ed

    2. Open analytics

    3. Download the Threads JSON

  2. Make a copy of this notebook

  3. Upload the data

  4. Adjust the filename and dates below

  5. Run all cells in the notebook

  6. Review the student contributions at the bottom

import pandas as pd

FILENAME = "FILENAME.json"
# dates are inclusive
START = pd.Timestamp(year=2024, month=4, day=17, tz="US/Eastern")
END = pd.Timestamp(year=2024, month=4, day=23, tz="US/Eastern")

Load data#

import json
from pathlib import Path
import pandas as pd

path = Path("..", FILENAME)
data = json.load(open(path))
threads = pd.json_normalize(data)
# threads

threads.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 97 entries, 0 to 96
Data columns (total 21 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   url             97 non-null     object
 1   type            97 non-null     object
 2   number          97 non-null     int64 
 3   title           97 non-null     object
 4   category        97 non-null     object
 5   subcategory     97 non-null     object
 6   subsubcategory  97 non-null     object
 7   votes           97 non-null     int64 
 8   views           97 non-null     int64 
 9   unique_views    97 non-null     int64 
 10  private         97 non-null     bool  
 11  anonymous       97 non-null     bool  
 12  endorsed        97 non-null     bool  
 13  created_at      97 non-null     object
 14  text            97 non-null     object
 15  document        97 non-null     object
 16  comments        97 non-null     object
 17  user.name       97 non-null     object
 18  user.email      97 non-null     object
 19  user.role       97 non-null     object
 20  answers         64 non-null     object
dtypes: bool(3), int64(4), object(14)
memory usage: 14.0+ KB

Include replies#

The JSON data includes reples (comments and answers) as nested under each post.

comments = pd.json_normalize(threads["comments"].explode().dropna())
# comments

replies = pd.json_normalize(threads["answers"].explode().dropna())
# replies

posts = pd.concat([threads, comments, replies]).reset_index()
# posts

posts["created_at"] = pd.to_datetime(posts["created_at"])
# posts["created_at"]

Filter#

output = posts[(posts["created_at"] >= START) & (posts["created_at"] <= END)]
print(output["created_at"].min())
print(output["created_at"].max())

2024-04-17 20:59:02.151711+10:00
2024-04-23 13:19:26.484477+10:00

Prep output#

# exclude the instructors
output = output[output["user.role"] != "admin"]

# sort by name
output = output.sort_values(["user.name", "created_at"])

# only include a subset of the columns
output = output[
    [
        "user.name",
        "url",
        # "created_at",
        # "title",
        "text",
    ]
]

# make links clickable
# https://stackoverflow.com/a/20043785/358804
output["url"] = output["url"].apply(lambda url: f'<a href="{url}">Open</a>')

# render newlines
# https://stackoverflow.com/a/56881411/358804
styled = output.style.set_properties(
    **{
        "text-align": "left",
        "white-space": "pre-wrap",
    }
)

Output#

from IPython.display import HTML

HTML(styled.to_html(escape=False))
Contents